1. Field of the Invention
The present invention relates to a radiographic image processing device and a radiographic image processing method which perform image processing including a scattered radiation removal process for a radiographic image and a program which causes a computer to perform the radiographic image processing method.
2. Description of the Related Art
In the related art, in the capture of a radiographic image of a subject using radiation that is transmitted through the subject, particularly, when the thickness of the subject is large, the radiation is scattered in the subject and the scattered radiation (hereinafter, also referred to a scattered ray) causes a reduction in the contrast of the acquired radiographic image. For this reason, in some cases, when a radiographic image is captured, a scattered radiation removal grid (hereinafter, simply referred to as a grid) is provided between a subject and a radiation detector which detects radiation and acquires a radiographic image such that the scattered radiation is not emitted to the radiation detector. When imaging is performed using the grid, radiation which is scattered by the subject is less likely to be emitted to the radiation detector. Therefore, it is possible to improve the contrast of the radiographic image.
In contrast, when imaging is performed using the grid, a subject image and a fine stripe pattern (moire) corresponding to the grid are included in the radiographic image, which makes it difficult to see the image.
Therefore, a method has been proposed which captures a radiographic image, without using a grid, and gives an image quality improvement effect, which can be obtained by removing scattered radiation, to the acquired radiographic image through image processing on the basis of imaging conditions (see U.S. Pat. No. 8,064,676B, JP2013-240696A, and C. Fivez et al., “Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation”, 1996 IEEE, pp. 339-342). The methods described in U.S. Pat. No. 8,064,676B and C. Fivez et al., “Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation”, 1996 IEEE, pp. 339-342 decompose a radiographic image into a plurality of frequency components, perform a process of controlling contrast for a low-frequency component which is regarded as a scattered radiation component, perform a contrast enhancement process for a high-frequency component in order to prevent a reduction in contrast due to the removal of the scattered radiation component, and combine the processed frequency components to acquire a radiographic image from which the scattered radiation component has been removed.
In the method described in U.S. Pat. No. 8,064,676B, the scattered radiation removal process is performed by multiplying a low-frequency component by a gain corresponding to the pixel value of the low-frequency component. Here, the gain is less than 1. The gain has a smaller value in a lower frequency band and is reduced as the pixel value increases. In contrast, a high-frequency component is multiplied by a gain of more than 1 to enhance the contrast of the radiographic image, in order to compensate for a reduction in contrast. In addition, the gain of a high-frequency component with high contrast is non-linearly controlled in order to prevent the over-enhancement of a high-contrast object (for example, a bone or metal) included in a radiographic image.
According to the methods disclosed in U.S. Pat. No. 8,064,676B, JP2013-240696A, and C. Fivez et al., “Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation”, 1996 TREE, pp. 339-342, since no grid is required during imaging, it is possible to reduce a burden on a patient during imaging. In addition, it is possible to remove scattered radiation and to obtain a radiographic image with improved contrast while preventing the deterioration of image quality due to density unevenness and moire.
It is known that, when a radiographic image of a subject is captured using radiation that is transmitted through the subject, the amount of radiation scattered in the subject increases and the influence on a reduction in radiation transmittance increases as the thickness of the subject increases, which results in a variation in the quality of the acquired radiographic image. For this reason, a technique has been proposed which roughly estimates the thickness of a subject, on the basis of various kinds of information, such as imaging conditions, a signal value of a radiographic image, the width of the histogram of the signal value of the radiographic image, and the length of the subject in a predetermined direction in the radiographic image, and changes the conditions of imaging processing, such as a scattered radiation removal process for the captured radiographic image, or imaging conditions applied to capture a radiographic image, on the basis of the estimated thickness of the subject.
For example, JP1990-244881A (JP-H02-244881A) discloses a method that measures pixel values of a radiograph image of a simulated subject with a known thickness which is captured under known imaging conditions, prepares a correspondence table in which a body thickness is associated with the pixel value in advance, roughly estimates a body thickness distribution on the basis of the pixel value of the radiographic image with reference to the correspondence table, estimates a scattered radiation component of the radiographic image corresponding to the body thickness distribution of the radiographic image, and subtracts the scattered radiation component from the radiographic image to acquire a processed image.
In addition, Trotter et al., “Thickness-dependent Scatter Correction Algorithm for Digital Mammography”, Proc. SPIE, Vol. 4682, May 2002, pp. 469-478 discloses a method which estimates a scattered radiation component of a radiographic image on the basis of a human body thickness distribution and removes the scattered radiation component. The method disclosed in Trotter et al., “Thickness-dependent Scatter Correction Algorithm for Digital Mammography”, Proc. SPIE, Vol. 4682, May 2002, pp. 469-478 applies a predetermined function to an input radiographic image on the basis of the body thickness distribution estimated from the pixel value of the radiographic image to generate an estimated scattered radiation image, which is obtained by estimating an image of scattered radiation included in the radiographic image, and subtracts the estimated scattered radiation image from the radiographic image to generate an estimated primary radiation image which is obtained by estimating a primary radiation image from the input radiographic image. In addition, the method repeatedly performs a process which applies a predetermined function to the generated estimated primary radiation image to generate an estimated scattered radiation image and subtracts the estimated scattered radiation image from the radiographic image to generate an estimated primary radiation image until the estimated scattered radiation image converges under predetermined convergence conditions, calculates a converged estimated scattered radiation image, and subtracts the estimated scattered radiation image from the radiographic image to finally obtain a processed image from which the scattered component has been removed. In addition, C. Fivez et al., “Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation”, 1996, IEEE, pp. 339-342 discloses a method which adjusts a predetermined function for estimating an image of scattered radiation included in the radiographic image according to the body thickness.
However, in the radiographic image, the attenuation of radiation which varies depending on the internal composition (a material and the density of the material) of the subject is represented by the shade of an image and the pixel value of the radiographic image corresponds to the amount of radiation transmitted through the subject. Here, when the amount of radiation which is incident on the subject is fin, a linear attenuation coefficient corresponding to the composition of radiation is μ, and a thickness is t, a value Iout of each pixel of a radiographic image is represented by Iout=lin·e−μt. A radiation detector performs logarithmic conversion for a signal acquired at each pixel position. Therefore, the value of each pixel of the radiographic image has a linear relationship with the logarithmic amount of radiation. That is, the value of each pixel can be represented by log(Iout)=log(Iin)+(−μt). Then, the radiographic image which has been subjected to the logarithmic conversion so as to have a linear relationship with the logarithmic amount of radiation is converted into a digital signal and image processing for improving image quality, such as a gradation process, frequency processing, and a dynamic range compression process, is performed to acquire a high-quality radiographic image.
Since the value of each pixel of the acquired radiographic image has a linear relationship with the amount of radiation subjected to logarithmic conversion, a difference in the thickness and composition of the subject can be reflected in the captured image of the subject without any change. Therefore, it is possible change the pixel value using a linear operation for the pixel value and thus to reproduce image processing, such as a gradation process, frequency processing, and a dynamic range compression process, with the same degree of enhancement, regardless of a radiographic image. For example, in the methods disclosed in U.S. Pat. No. 8,064,676B and C. Fivez et al., “Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation”, 1996, IEEE, pp. 339-342, a low-frequency component is multiplied by a gain of less than 1 to suppress a scattered radiation component and a high-frequency component is multiplied by a gain of more than 1 to enhance contrast. This method can be used since the radiographic image has a linear relationship with the logarithmic amount of radiation. Therefore, when image processing is performed for the radiographic image which has a linear relationship with the amount of radiation subjected to logarithmic conversion, it is possible to acquire a high-quality radiographic image suitable for diagnosis, regardless of imaging conditions.